论文标题
神经形态计算中的可靠性绩效权衡
Reliability-Performance Trade-offs in Neuromorphic Computing
论文作者
论文摘要
使用非易失性记忆（NVM）构建的神经形态架构可以显着提高使用尖峰神经网络（SNN）设计的机器学习任务的能效。这些体系结构的横杆中电压下降的主要来源是横杆的位点和文字线上的寄生成分，故意使它们更长的时间以达到较低的每位成本。我们观察到，寄生电压下降在横杆中NVM细胞的编程速度和可靠性上产生了明显的不对称性。具体而言，在较短的电流路径上的NVM单元格的编程速度更快，但耐力比较长的电流路径上的耐力较低，反之亦然。这种神经形态体系结构中的不对称性创造了可靠性 - 性能折衷，可以使用SNN映射技术有效利用它们。在这项工作中，我们使用以前提供的SNN映射技术展示了此类权衡，并从当代机器学习任务中完成了10项工作负载，用于最先进的神经型硬件。
Neuromorphic architectures built with Non-Volatile Memory (NVM) can significantly improve the energy efficiency of machine learning tasks designed with Spiking Neural Networks (SNNs). A major source of voltage drop in a crossbar of these architectures are the parasitic components on the crossbar's bitlines and wordlines, which are deliberately made longer to achieve lower cost-per-bit. We observe that the parasitic voltage drops create a significant asymmetry in programming speed and reliability of NVM cells in a crossbar. Specifically, NVM cells that are on shorter current paths are faster to program but have lower endurance than those on longer current paths, and vice versa. This asymmetry in neuromorphic architectures create reliability-performance trade-offs, which can be exploited efficiently using SNN mapping techniques. In this work, we demonstrate such trade-offs using a previously-proposed SNN mapping technique with 10 workloads from contemporary machine learning tasks for a state-of-the art neuromoorphic hardware.
